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Details of Grant 

EPSRC Reference: EP/K005952/1
Title: Human Vision: Relationship to Three-Dimensional Surface Statistics of Natural Scenes
Principal Investigator: Adams, Professor WJ
Other Investigators:
Leyland, Dr J Graf, Dr EW
Researcher Co-Investigators:
Project Partners:
Department: Sch of Psychology
Organisation: University of Southampton
Scheme: Standard Research
Starts: 30 June 2013 Ends: 29 December 2016 Value (£): 505,830
EPSRC Research Topic Classifications:
Image & Vision Computing Vision & Senses - ICT appl.
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
09 Oct 2012 EPSRC ICT Responsive Mode - Oct 2012 Announced
Summary on Grant Application Form
The human visual system has been fine-tuned over generations of evolution to operate effectively in our particular environment, allowing us to form rich 3D representations of the objects around us. The scenes that we encounter on a daily basis produce 2D retinal images that are complex and ambiguous. From this input, how does the visual system achieve the immensely difficult goal of recovering our surroundings, in such an impressively fast and robust way?

To achieve this feat, humans must use two types of information about their environment. First, we must learn the probabilistic relationships between 3D natural scene properties and the 2D image cues these produce. Second, we must learn which scene structures (shapes, distances, orientations) are most common, or probable in our 3D environment. This statistical knowledge about natural 3D scenes and their projected images allows us to maximize our perceptual performance. To better understand 3D perception, therefore, we must study the environment that we have evolved to process. A key goal of our research is to catalogue and evaluate the statistical structure of the environment that guides human depth perception. We will sample the range of scenes that humans frequently encounter (indoor and outdoor environments over different seasons and lighting conditions). For each scene, state-of-the-art ground based Light Detection and Ranging (LiDAR) technology will be used to measure the physical distance to all objects (trees, ground, etc.) from a single location - a 3D map of the scene. We will also take High Dynamic Range (HDR) photographs of the same scene, from the same vantage point. By collating this paired 3D and 2D data across numerous scenes we will create a comprehensive database of our environment, and the 2D images that it produces. By making the database publicly available it will facilitate not just our own work, but research by human and computer vision scientists around the world who are interested in a range of pure and applied visual processes.

There is great potential for computer vision to learn from the expert processor that is the human visual system: computer vision algorithms are easily out-performed by humans for a range of tasks, particularly when images correspond to more complex, realistic scenes. We are still far from understanding how the human visual system handles the kind of complex natural imagery that defeats computer vision algorithms. However, the robustness of the human visual system appears to hinge on: 1) exploiting the full range of available depth cues and 2) incorporating statistical 'priors': information about typical scene configurations. We will employ psychophysical experiments, guided by our analyses of natural scenes and their images, to develop valid and comprehensive computational models of human depth perception. We will concentrate our analysis and experimentation on key tasks in the process of recovering scene structure - estimating the location, orientation and curvature of surface segments across the environment. Our project addresses the need for more complex and ecologically valid models of human perception by studying how the brain implicitly encodes and interprets depth information to guide 3D perception.

Virtual 3D environments are now used in a range of settings, such as flight simulation and training systems, rehabilitation technologies, gaming, 3D movies and special effects. Perceptual biases are particularly influential when visual input is degraded, as they are in some of these simulated environments. To evaluate and improve these technologies we require a better understanding of 3D perception. In addition, the statistical models and inferential algorithms developed in the project will facilitate the development of computer vision algorithms for automatic estimation of depth structure in natural scenes. These algorithms have many applications, such as 2D to 3D film conversion, visual surveillance and biometrics.
Key Findings
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Organisation Website: http://www.soton.ac.uk